What measure of central tendency is calculated by taking the sum of the data then dividing by n?

Measures of Central Tendency is another majorly used Descriptive statistics. Measures of Central Tendency tries to summarise the data by a single value that represents the middle of the distribution. There are three Measures of Central Tendency: Mean, Median and Mode.

Contents

• 1 Mean
• 2 Median
• 3 Mode
• 4 Outliers and Measures of Central Tendency
  • 4.1 Outliers and Mean
  • 4.2 Outliers and Median
  • 4.3 Outliers and Mode

Mean

Mean is the arithmetic average of a distribution of scores also known as the arithmetic average. It provides a rough summary of the distribution, however, it does not tell anything about the spread of the distribution. Mean can be calculated by taking the sum of all the observations in a dataset and dividing it by the number of observations i.e.

where,

S= the sum of observations in the set of interest

N= the number of observations

For example, there is a dataset with 5 values-

96 ,94, 92, 87, 81

We can calculate the mean of this dataset by adding all the values(S=450) and dividing it by the number of observations (N=10).

Here the arithmetic mean of this data set will be 90.

There are certain advantages and disadvantages with mean. While the mean can provide a quick summary of a dataset and can be used for continuous as well as discrete datasets, it cannot be used for qualitative data (Categorical variables). Also, mean is highly sensitive to extreme values and can provide a very biased outcome if there are outliers in the data. Also, the shape of the distribution can also influence the outcome of mean and especially if the distribution is skewed, the mean can provide misleading results. Mean also fails in explaining the spread of the scores (variance) and the number of scores in a distribution close to the mean.

In the introduction of this section, the difference between a parameter and a statistic was explained.

When mean is calculated from a population dataset then the mean is a parameter denoted by the symbol μ however if mean is calculated from a subset of this population i.e. from a sample then the calculated mean is a statistic denoted by the symbol x̅. Therefore the formula for calculating Population mean will be

μ = ( Σ Xi ) / N   

while the formula for calculating Sample Mean will be

x̅= ( Σ xi ) / n

where x̅ is the sample mean,

μ is the population mean,

Σ means “the sum of”

X is an individual score in the population distribution

x is an individual score in the sample distribution

 n is the number of scores in the sample

N is the number of scores in the population

Median

Median of a given dataset can be found when the values of the dataset are arranged in ascending or descending order and the middle value or the value that marks the 50th percentile of this dataset’s distribution is called its Median. The Median divides and distributes the dataset into two equal halves with 50% of observations on each side.

In the below-mentioned example we have 11 values and when they are sorted from smallest or largest then the value that falls in the middle is the median (in green).

However, if the number of values is even then in that case we have to calculate the mean by taking an average of the middle two values.

There are certain advantages that median has over mean and the main ones are that median doesn’t get affected by outliers or if the distribution of data is skewed. Median, however, cannot be used for certain type of categorical data such as Nominal Categorical Data as they can not be ordered on the basis of their weights as the values do not have any weight and cannot be ordered logically.

The Mean and Median are the measures of Central Tendency that are some of the most useful statistics as they provide information about the whole distribution in a single number however it is very important to note that they ignore a great deal of information about the distribution making it easy for them to be misused and make sweeping generalisations based on mean or median.

Mode

Mode can be calculated by calculating the occurrence of scores in a distribution. It is among the least used Measure of Central Tendency because it does not provide much information.

For example, there is a dataset having the number of cars owned by a household. In the below example, we surveyed 20 houses and the mode is the number that gets repeated the most often.

Mode is more useful than mean or median in the sense that it can be used for both numerical as well as categorical data but suffers when there are no repetitions among the values (Continuous Numerical Data) or if there is more than one mode in a distribution(Bi-Modal or Multi-Modal Distributions). Under such circumstances the descriptive capabilities of mode gets limited.

Outliers and Measures of Central Tendency

In all the three Measures of Central Tendency, it is mentioned how they may or may not be vulnerable to outliers. Here we explore this with examples explaining how Mean, Median and Mode may or may not get affected by Outliers.

Outliers and Mean

Here it is very evident how due to one unproportional value, the mean gets affected. Thus if in a dataset there are values that are too large or too small than the rest of the values, then it can cause the mean to deviate.

Outliers and Median

We can see how median is not affected by the outlier as when the data is sorted, the outlier gets either in the beginning (if the outlier is very small in weight) or in the end (if the value of outlier is too large), and the middle value remains intact.

Outliers and Mode

We add an outlier in the earlier used example for mode and find that mode too does not get affected by outliers.

Therefore among all the Measures of Central Tendency, mean is most vulnerable to outliers while others are less vulnerable to them.

Thus Measures of Central Tendency helps us in understanding our data by providing us with one value. Various Measures of Central Tendency can be used with then Mean being used for numerical while the Mode being used for categorical data. However they all fail to explain the distribution of data as for example, a variable having a value 6 for six times time will have a mean of 6 and a variable having values 2, 3, 4, 6, 8 and 13 will also have a mean of six. Therefore it becomes very important to understand the distribution of the data and this leads us to Measures of Variability using which we can understand the spread of the data. 

Which measure of central tendency is computed by dividing the sum of the data by the number of pieces of data?

Which measure of central tendency is obtained by calculating the sum of values and dividing this figure by the number of values there are in the data set?

Which measure of central tendency is calculated by summing all of the scores and dividing by the number of scores?

What is the central measure formed by adding all numbers in the dataset and then dividing by the number of values?